Research Themes

Material science is a dynamic field at the forefront of innovation bridging fundamental research and practical applications.

Electronic structure

Electronic structure refers to how electrons are arranged within a material, influencing properties like conductivity and strength of a material. Electronic structure is studied using methods such as DFT.

Machine learning

Machine learning, a subset of artificial intelligence, involves training algorithms on large datasets to identify patterns and make predictions, reducing the need for extensive experimental trials. In material science, it is applied to predict crystal structures, material properties, and discover new materials with specific characteristics. Common algorithms include neural networks, support vector machines, and random forests. These models are trained on datasets from sources like the Materials Project, enabling predictions with high accuracy in fields such as superconductivity, thermoelectrics, and catalysis.

High throughput computing

High throughput calculations involve automating computational processes to screen a large number of materials or parameter sets efficiently, often using methods like Density Functional Theory (DFT). The methodology includes setting up and running multiple simulations in parallel, with specific parameters, enabling the identification of promising candidate materials and in turn facilitating focused experimental efforts.

High perfomance computing

HPC involves aggregating computing power to deliver performance far beyond typical desktop computers, essential for simulations requiring significant processing power, such as quantum mechanical calculations, molecular dynamics, and large-scale data analysis. It supports parallel processing and is characterized by high-speed processing power, high-performance networks, and large-memory capacity. In material science, HPC is crucial in powering complex simulations such as DFT calculations for electronic structure.